This article analyzes the technical nuances and legal implications of using synthetic data, which uses machine learning techniques to modify raw data, as an alternative to anonymization or differential privacy to protect privacy interests while facilitating research.
Nathan Reitinger
Nathan Reitinger is a late-stage PhD candidate at the University of Maryland in the Department of Computer Science where he works on problems lying at the intersection of law and computer science. He has written extensively on data sanitization, machine learning, cryptography, autonomous weapons systems, and the intellectual property rights for 3D printing. He holds an M.S. from Columbia University and a J.D., magna cum laude, from Michigan State University.